{"meta": {"boss": "Cagney Carnation", "loadout": "Peashooter + Smoke Bomb", "difficulty": "Regular", "start_utc": "2025-09-23T13-52-06.960065+00:00"}}
{"event": "keydown", "key": "f", "t_ms": 1476}
...
{"event": "keydown", "key": "Key.space", "t_ms": 4038}
{"event": "keyup", "key": "Key.space", "t_ms": 4054}
{"summary": {"outcome": "win", "duration_ms": 74788, "end_utc": "2025-09-23T13:53:21.751994+00:00"}}

{'summary': {'outcome': 'win',
  'duration_ms': 74788,
  'end_utc': '2025-09-23T13:53:21.751994+00:00'}}

# Import Libraries
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import json
import glob

# Import Machine Learning Libraries
from matplotlib.colors import LogNorm
import matplotlib.patches as mpatches
from sklearn.feature_extraction.text import CountVectorizer
from scipy.sparse import hstack
from sklearn.model_selection import GroupKFold, LeaveOneGroupOut
from matplotlib.colors import ListedColormap
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from xgboost import XGBClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import cross_val_predict
from sklearn.metrics import classification_report
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.base import clone
from scipy.sparse import vstack as sparse_vstack

# Ignore Warnings
import warnings
warnings.filterwarnings('ignore', category=FutureWarning)
warnings.filterwarnings('ignore', category=UserWarning)
warnings.filterwarnings('ignore', category=DeprecationWarning)


# Set a random seed for reproducibility of the sampling
np.random.seed(34) # my birthday

# 1. Load All Raw Data
data_path = '../data/raw/*.jsonl'
files = glob.glob(data_path)
all_events_data = []

# Iterate through each file and extract event data
for file_path in files:
    with open(file_path, 'r') as f:
        # Read all lines from the file
        lines = f.readlines()
        if not lines: continue
        try:
            meta_data = json.loads(lines[0])['meta']
            fight_id = json.loads(lines[0])['fight_id']
            boss_name = meta_data.get('boss')
            for line in lines[1:-1]:
                event_data = json.loads(line)
                if 'event' in event_data:
                    all_events_data.append({
                        'fight_id': fight_id, 'boss': boss_name,
                        'event': event_data['event'], 'key': event_data['key'],
                        't_ms': event_data['t_ms']
                    })
        except (json.JSONDecodeError, KeyError) as e:
            print(f"Skipping malformed file: {file_path}")

# Convert the list of event data into a DataFrame
raw_df = pd.DataFrame(all_events_data)
print(f"Loaded a total of {raw_df['fight_id'].nunique()} fights initially.")


# 2. Adjust Timestamps
first_action_times = raw_df.groupby('fight_id')['t_ms'].transform('min')
clean_df = raw_df.copy()
# Subtract the first action time from all timestamps in that fight to normalize them
clean_df['t_ms'] = clean_df['t_ms'] - first_action_times
print("Normalized all fight timestamps to start at t_ms = 0 from the first action.")


# 3. Filter Out Accidental Recordings
fight_stats = clean_df.groupby('fight_id')['t_ms'].agg(
    event_count='count', 
    duration_ms='max'
)
# Identify the IDs of fights that are long enough and have enough events
valid_fight_ids = fight_stats[
    (fight_stats['event_count'] >= 10) & 
    (fight_stats['duration_ms'] >= 5000)
].index
# Filter the main DataFrame to keep only valid fights
clean_df = clean_df[clean_df['fight_id'].isin(valid_fight_ids)]
print(f"Filtered down to {len(valid_fight_ids)} valid fights (>=10 events and >=10s duration).")

all_sampled_ids = []

# Get unique fight IDs along with their corresponding boss names
valid_fights_with_boss = clean_df[['fight_id', 'boss']].drop_duplicates()

for boss_name in valid_fights_with_boss['boss'].unique():
    # Get all valid fight IDs for the current boss
    boss_fight_ids = valid_fights_with_boss[valid_fights_with_boss['boss'] == boss_name]['fight_id'].unique()
    
    # Print the count before sampling
    total_fights_for_boss = len(boss_fight_ids)
    print(f"- {boss_name}: 60 fights chosen out of {total_fights_for_boss} fights")
    
    # Randomly choose 60 IDs from this boss's pool
    sampled_boss_ids = np.random.choice(boss_fight_ids, size=60, replace=False)
    
    # Add these sampled IDs to the overall list
    all_sampled_ids.extend(sampled_boss_ids)

# Filter the main DataFrame to keep only the 240 sampled fights
final_df = clean_df[clean_df['fight_id'].isin(all_sampled_ids)].reset_index(drop=True)

print(f"\nFinal balanced dataset contains {final_df['fight_id'].nunique()} fights.")
final_df.head(10)

Loaded a total of 278 fights initially.
Normalized all fight timestamps to start at t_ms = 0 from the first action.
Filtered down to 277 valid fights (>=10 events and >=10s duration).
- Glumstone the Giant: 60 fights chosen out of 82 fights
- Baroness Von Bon Bon: 60 fights chosen out of 60 fights
- Cagney Carnation: 60 fights chosen out of 74 fights
- Grim Matchstick: 60 fights chosen out of 61 fights

Final balanced dataset contains 240 fights.

# Calculate Press Durations for all fights
def calculate_all_press_durations(df):
    """Matches every keydown to its corresponding keyup to get press durations for all fights."""
    downs = df[df['event'] == 'keydown'].copy()
    ups = df[df['event'] == 'keyup'].copy()
    
    # Create a unique ID for each press of a specific action within a fight
    downs['press_num'] = downs.groupby(['fight_id', 'action']).cumcount()
    ups['press_num'] = ups.groupby(['fight_id', 'action']).cumcount()
    
    # Merge them together to align the start and end time of each press
    merged = pd.merge(downs, ups, on=['fight_id', 'boss', 'action', 'press_num'], suffixes=('_down', '_up'))
    
    # Calculate duration and filter out any potential negative durations from data errors
    merged['duration_ms'] = merged['t_ms_up'] - merged['t_ms_down']
    return merged[merged['duration_ms'] >= 0][['fight_id', 'action', 'duration_ms']]

press_durations_df = calculate_all_press_durations(processed_df)
print("Sample of calculated press durations:")
print(press_durations_df.head())

# Foundational Feature Engineering
# Part A: Pace & Frequency Features (Action per minute (APM) and action percentages)
keydowns = processed_df[processed_df['event'] == 'keydown']
fight_summaries = keydowns.groupby('fight_id').agg(
    duration_ms=('t_ms', 'max'),
    event_count=('t_ms', 'count'),
    boss=('boss', 'first')
)
# Calculate action per minute
fight_summaries['apm'] = (fight_summaries['event_count'] / (fight_summaries['duration_ms'] / 1000.0)) * 60

# Calculate action percentages
action_percentages = keydowns.groupby(['fight_id', 'action']).size().unstack(fill_value=0).div(fight_summaries['event_count'], axis=0).add_prefix('pct_')

# Part B: Overall Rhythm Features (overall press duration statistics)
overall_duration_stats = press_durations_df.groupby('fight_id')['duration_ms'].agg(
    ['mean', 'std', 'median', 'min', 'max']
).add_prefix('overall_duration_')

# Part C: Action-Specific Timing Features (mean & std of press durations per action)
action_specific_stats = press_durations_df.groupby(['fight_id', 'action'])['duration_ms'].agg(['mean', 'std']).unstack().fillna(0)
# Flatten the multi-level column names (('mean', 'jump') -> 'mean_jump')
action_specific_stats.columns = ['_'.join(col).strip() for col in action_specific_stats.columns.values]

# Combine all foundational features into a single DataFrame
agg_features = fight_summaries.join(action_percentages).join(overall_duration_stats).join(action_specific_stats).fillna(0)

# Separate features (X) from the target variable (y)
X_agg = agg_features.drop(columns=['boss', 'duration_ms', 'event_count'])
y_str = agg_features['boss'] # Keep original string labels for plotting

# Encode target variable
le = LabelEncoder()
y = pd.Series(le.fit_transform(y_str), index=y_str.index, name='boss_encoded')

# Store the number of classes for the model later
NUM_CLASSES = len(le.classes_)

print("--- Boss to Integer Mapping ---")
for i, class_name in enumerate(le.classes_):
    print(f"{class_name}: {i}")

print(f"\nShape of feature matrix (X_agg): {X_agg.shape}")
print(f"Shape of target vector (y): {y.shape}")
print(f"Target 'y' is now integer-encoded.")
print("\nFirst 5 rows of the feature matrix:")
X_agg.head()

Sample of calculated press durations:
                     fight_id action  duration_ms
0  2025-09-24T10-17-07Z_27194  shoot          403
1  2025-09-24T10-17-07Z_27194   down          287
2  2025-09-24T10-17-07Z_27194   down          791
3  2025-09-24T10-17-07Z_27194  shoot          866
4  2025-09-24T10-17-07Z_27194   down         1087
--- Boss to Integer Mapping ---
Baroness Von Bon Bon: 0
Cagney Carnation: 1
Glumstone the Giant: 2
Grim Matchstick: 3

Shape of feature matrix (X_agg): (240, 33)
Shape of target vector (y): (240,)
Target 'y' is now integer-encoded.

First 5 rows of the feature matrix:

# Define the max time gap for an action to be part of a combo
COMBO_THRESHOLD_MS = 1000 

# Create a combined feature set with N-grams capturing action sequences
def create_combo_sentences(group):
    """
    Creates a string of actions, but inserts a '.' to break up combos
    whenever the time between actions is greater than the threshold.
    """
    # Ensure the group is sorted by time
    group = group.sort_values('t_ms')
    
    # Calculate the time difference between each action and the previous one
    time_diffs = group['t_ms'].diff().fillna(0)
    
    # Start the sentence with the first action
    sentence = [group['action'].iloc[0]]
    
    # Iterate from the second action onwards
    for i in range(1, len(group)):
        # If the gap is too large, insert a period to denote a break in the combo
        if time_diffs.iloc[i] > COMBO_THRESHOLD_MS:
            sentence.append('.')
        sentence.append(group['action'].iloc[i])
        
    return ' '.join(sentence)

# create a combined action sentences for each fight_id group for only keydown events
fight_actions_str = keydowns.groupby('fight_id').apply(create_combo_sentences)

# Engineer N-gram Features from the Combo Sentences
# The CountVectorizer will naturally treat the '.' as a word boundary

# get top 50 most frequent 2-grams and 3-grams
vectorizer = CountVectorizer(ngram_range=(2, 3), max_features=50)
ngram_features_sparse = vectorizer.fit_transform(fight_actions_str)

print("A few of the top time-aware action combos (N-grams) found:")
print(vectorizer.get_feature_names_out()[:10])


# Create the Final Enriched Feature Set (X_enriched)
# Ensure the rows of X_agg are in the same order as the fight_actions_str
X_agg_ordered = X_agg.loc[fight_actions_str.index]

# Combine the aggregated features with the N-gram features
X_enriched = hstack([X_agg_ordered.values, ngram_features_sparse]).tocsr()

print(f"\nSuccessfully created the enriched feature set with shape: {X_enriched.shape}")

A few of the top time-aware action combos (N-grams) found:
['attack jump' 'attack right' 'attack right jump' 'dash left' 'dash right'
 'down jump' 'down jump right' 'jump jump' 'jump jump jump'
 'jump jump left']

Successfully created the enriched feature set with shape: (240, 83)

# 1. Create a DataFrame of N-gram Frequencies with Labels

# Get the names of the 50 N-gram features from the vectorizer
ngram_names = vectorizer.get_feature_names_out()

# Create a dense pandas DataFrame from the sparse matrix
# Use the fight_id index from fight_actions_str to correctly label each row
ngram_df = pd.DataFrame(ngram_features_sparse.toarray(), columns=ngram_names, index=fight_actions_str.index)
ngram_df['boss'] = y_str

# 2. Calculate the Mean Frequency of Each N-gram per Boss
# get the average number of times each combo appears per fight for each boss
mean_ngram_freq = ngram_df.groupby('boss').mean()


# 3. Visualize the Top 5 N-grams for Each Boss
boss_names = mean_ngram_freq.index
n_bosses = len(boss_names)

# Prepare Color Mapping
boss_palette = sns.color_palette("husl", n_bosses)
boss_color_map = dict(zip(boss_names, boss_palette))

# Create subplots, one for each boss
fig, axes = plt.subplots(n_bosses, 1, figsize=(10, 14), sharex=True)
fig.suptitle('Top 5 Most Frequent Action Combos (N-grams) by Boss', fontsize=16, y=1.02)

for i, boss_name in enumerate(boss_names):
    # Get the top 5 combos for the current boss
    top_5 = mean_ngram_freq.loc[boss_name].nlargest(5)
    
    # Create the horizontal bar plot
    sns.barplot(
        x=top_5.values, 
        y=top_5.index, 
        ax=axes[i], 
        color=boss_color_map[boss_name],
        orient='h'
    )
    
    axes[i].set_title(f'Most Frequent Combos for: {boss_name}', fontsize=12)
    axes[i].set_xlabel('Average Occurrences per Fight')
    axes[i].set_ylabel('Action Combo (N-gram)')

plt.tight_layout()
plt.show()

# 1. Get the Full List of Feature Names and a Single Vector

# get the names of all features in the enriched feature set
agg_feature_names = X_agg.columns.tolist()
ngram_feature_names = vectorizer.get_feature_names_out()
all_feature_names = agg_feature_names + list(ngram_feature_names)
# extract the feature vector for a single fight (ex. the first fight in the dataset)
single_fight_vector_sparse = X_enriched[0]
single_fight_vector_dense = single_fight_vector_sparse.toarray().flatten()
feature_series = pd.Series(single_fight_vector_dense, index=all_feature_names, name="Feature Value")


# 2. Split the Vector into its Two Logical Parts
# Part 1: Aggregate and Duration Statistics
agg_series = feature_series.loc[agg_feature_names]
# For log plot, we replace 0 with a very small number to avoid errors
agg_series_log = agg_series.replace(0, 0.001)
# Part 2: N-gram Combo Counts
ngram_series_full = feature_series.loc[ngram_feature_names]


# 3. Create the Two-Part Visualization
fig, axes = plt.subplots(1, 2, figsize=(18, 10))
fig.suptitle(f"Complete Feature Fingerprint for Fight ID: {y.index[0]}", fontsize=18)

# --- Plot 1: The Statistical Fingerprint ---
sns.barplot(
    x=agg_series_log.values, 
    y=agg_series_log.index, 
    orient='h', 
    ax=axes[0],
    color='steelblue'
)
axes[0].set_title('Statistical Fingerprint (Log Scale)', fontsize=14)
# Use a log scale to show values of different magnitudes
axes[0].set_xscale('log') 
axes[0].set_xlabel('Feature Value (Log Scale)')
axes[0].grid(axis='x')

# --- Plot 2: The Combo Fingerprint ---
sns.barplot(
    x=ngram_series_full.values, 
    y=ngram_series_full.index, 
    orient='h', 
    ax=axes[1],
    color='mediumseagreen'
)
axes[1].set_title('Full Combo Fingerprint (All 50 N-grams)', fontsize=14)
axes[1].set_xlabel('Number of Occurrences in Fight')
# Set x-axis ticks to be integers for counts
axes[1].xaxis.set_major_locator(plt.MaxNLocator(integer=True))

plt.tight_layout(rect=[0, 0.03, 1, 0.95])
plt.show()

# --- 1. Analyze Boss Distribution Within Each Group ---
groups = y.index.str[:10]
unique_groups = np.unique(groups)

# Create a DataFrame to hold the counts of each boss per session
group_dist = y.groupby([groups, y]).size().unstack(fill_value=0)
print("--- Boss Distribution per Play Session ---")
display(group_dist)


# --- 2. Intelligently Select the Best Test Groups ---
from itertools import combinations

best_combo = None
min_imbalance = float('inf')

# Iterate through all possible combinations of 2 groups for the test set
for combo in combinations(unique_groups, 2):
    # Calculate the boss distribution for this potential test set
    test_dist = group_dist.loc[list(combo)].sum()
    
    # We want the distribution to be as close to equal as possible.
    # We can measure imbalance by the standard deviation of the counts.
    imbalance = test_dist.std()
    
    if imbalance < min_imbalance:
        min_imbalance = imbalance
        best_combo = list(combo)

test_groups = best_combo
dev_groups = [g for g in unique_groups if g not in test_groups]

print("\n--- Selected Top-Level Data Split ---")
print(f"Selected Test Set Groups: {test_groups}")
print(f"Remaining Development Set Groups: {dev_groups}")
print("\nTest set distribution:")
display(group_dist.loc[test_groups].sum())


# --- 3. Create the Final Data Splits ---
dev_mask = groups.isin(dev_groups)
test_mask = groups.isin(test_groups)

# The final, untouched test sets (for Section 7.1)
X_test_agg, y_test = X_agg[test_mask], y[test_mask]
X_test_enriched = X_enriched[test_mask]

# The development sets (for Section 6 cross-validation)
X_dev_agg, y_dev = X_agg[dev_mask], y[dev_mask]
X_dev_enriched = X_enriched[dev_mask]
groups_dev = groups[dev_mask]

print("\n--- Final Dataset Shapes ---")
print(f"Development set size: {len(y_dev)} samples ({len(dev_groups)} sessions)")
print(f"Test set size:  {len(y_test)} samples ({len(test_groups)} sessions)")

# --- 4. Define CV Strategies for the DEVELOPMENT set ---
cv_5fold = GroupKFold(n_splits=5)
cv_logo_dev = LeaveOneGroupOut() # Will create 6 folds for the 6 development groups

--- Boss Distribution per Play Session ---

--- Selected Top-Level Data Split ---
Selected Test Set Groups: ['2025-09-24', '2025-10-04']
Remaining Development Set Groups: ['2025-09-23', '2025-09-26', '2025-09-27', '2025-09-29', '2025-10-08', '2025-10-14']

Test set distribution:

boss_encoded
0    14
1    18
2    24
3    15
dtype: int64

--- Final Dataset Shapes ---
Development set size: 169 samples (6 sessions)
Test set size:  71 samples (2 sessions)

FUNCTION train_logistic_regression(X, y, learning_rate, epochs, lambda):
    """
    Trains a Multinomial Logistic Regression model using Gradient Descent.

    Args:
        X (matrix): Training data of shape (m_samples, n_features).
        y (vector): True labels, one-hot encoded, of shape (m_samples, K_classes).
        learning_rate (float): Step size for weight updates.
        epochs (int): Number of passes over the training data.
        lambda (float): L2 regularization strength.
    """
    
    // 1. Initialize weights and biases with small random values or zeros.
    m, n = shape(X)
    K = number_of_classes
    W = new Matrix(n, K) of zeros
    b = new Vector(K) of zeros
    
    // 2. Loop for a number of epochs to iteratively improve the weights.
    FOR epoch FROM 1 TO epochs:
        // 3. Forward Pass: Make predictions with current weights.
        //    a. Calculate linear scores (logits) for each class.
        logits = X @ W + b
        //    b. Apply softmax to get probabilities.
        probabilities = softmax(logits)
        
        // 4. Gradient Calculation: Find the direction to update weights.
        error = probabilities - y
        
        //    Calculate gradients for weights and biases, including the regularization term for weights.
        grad_W = (1/m) * (X.T @ error) + (lambda/m) * W
        grad_b = (1/m) * sum(error)
        
        // 5. Weight Update: Take a small step against the gradient.
        W = W - learning_rate * grad_W
        b = b - learning_rate * grad_b
        
    RETURN W, b

FUNCTION train_gradient_boosting(X, y, learning_rate, n_estimators):
    """
    Trains a Gradient Boosting model with regression trees for classification.
    (This is a simplified illustration of the core XGBoost logic).

    Args:
        X (matrix): Training data.
        y (vector): True labels (e.g., as integers 0, 1, 2, 3).
        learning_rate (float): Shrinks the contribution of each tree.
        n_estimators (int): The total number of trees to build.
    """
    
    // 1. Initialize the model with a simple base prediction.
    //    For classification, this is often the log-odds of the base class frequency.
    F_0 = calculate_initial_prediction(y)
    ensemble_prediction = F_0
    
    // 2. Loop for the number of estimators (trees) to build.
    FOR m FROM 1 TO n_estimators:
        // 3. Compute Pseudo-Residuals:
        //    Calculate the error of the current ensemble. This is the negative
        //    gradient of the loss function. For classification, this is (true_label - predicted_prob).
        current_probabilities = softmax(ensemble_prediction)
        pseudo_residuals = y - current_probabilities
        
        // 4. Train a new tree:
        //    Fit a simple Regression Tree (h_m) to predict the pseudo-residuals.
        //    This tree learns to correct the errors of the current ensemble.
        h_m = fit_regression_tree(X, pseudo_residuals)
        
        // 5. Update the ensemble:
        //    Add the new tree's predictions, scaled by the learning rate,
        //    to the overall ensemble prediction.
        ensemble_prediction = ensemble_prediction + learning_rate * h_m.predict(X)
        
    RETURN the final ensemble of trees

CLASS Pipeline:
    """
    A simplified pseudocode representation of a scikit-learn Pipeline.

    Attributes:
        steps (list): A list of (name, transformer/estimator) tuples.
                      e.g., [('scaler', StandardScaler), ('logreg', LogisticRegression)]
    """
    
    FUNCTION fit(self, X_train, y_train):
        """
        Fits all transformers and the final estimator sequentially.

        Args:
            X_train (matrix): The training feature data.
            y_train (vector): The training labels.
        """
        
        // Start with the original training data.
        transformed_X = X_train
        
        // Loop through all steps EXCEPT the final one (the model).
        FOR each step (name, transformer) in self.steps[:-1]:
            // Fit the transformer on the current data and transform it.
            // The output becomes the input for the next step.
            transformed_X = transformer.fit_transform(transformed_X, y_train)
            
        // For the final step (the model), just call .fit().
        final_model = self.steps[-1][1]
        final_model.fit(transformed_X, y_train)
        
        RETURN self

    FUNCTION predict(self, X_new):
        """
        Transforms the new data and makes a prediction using the fitted model.

        Args:
            X_new (matrix): The new, unseen data to predict on.
        """
        
        // Start with the new, unseen data.
        transformed_X = X_new
        
        // Loop through all steps EXCEPT the final one (the model).
        FOR each step (name, transformer) in self.steps[:-1]:
            // Use the ALREADY FITTED transformer to just .transform() the new data.
            // No new fitting occurs here, preventing data leakage.
            transformed_X = transformer.transform(transformed_X)
            
        // For the final step (the model), call .predict() on the transformed data.
        final_model = self.steps[-1][1]
        prediction = final_model.predict(transformed_X)
        
        RETURN prediction

# Baseline Model: Logistic Regression
lr_pipeline = Pipeline([
    # Use with_mean=False because the enriched feature set is a sparse matrix.
    ('scaler', StandardScaler(with_mean=False)), 
    ('logreg', LogisticRegression(
        multi_class='multinomial', 
        solver='lbfgs', 
        random_state=42,
        max_iter=1000
    ))
])

# Advanced Model: XGBoost Classifier
# Pipeline maintains a consistent workflow
xgb_pipeline = Pipeline([
    ('scaler', StandardScaler(with_mean=False)),
    ('xgb', XGBClassifier(
        objective='multi:softprob',
        eval_metric='mlogloss',
        random_state=42,
        num_class=NUM_CLASSES,
        use_label_encoder=False
    ))
])

print("Baseline Model:")
print(lr_pipeline)
print("\nAdvanced Model:")
print(xgb_pipeline)

Baseline Model:
Pipeline(steps=[('scaler', StandardScaler(with_mean=False)),
                ('logreg',
                 LogisticRegression(max_iter=1000, multi_class='multinomial',
                                    random_state=42))])

Advanced Model:
Pipeline(steps=[('scaler', StandardScaler(with_mean=False)),
                ('xgb',
                 XGBClassifier(base_score=None, booster=None, callbacks=None,
                               colsample_bylevel=None, colsample_bynode=None,
                               colsample_bytree=None, device=None,
                               early_stopping_rounds=None,
                               enable_categorical=False, eval_metric='mlogloss',
                               feature_types=None, feature_weights=None,
                               gamma=None, grow_policy=None,
                               importance_type=None,
                               interaction_constraints=None, learning_rate=None,
                               max_bin=None, max_cat_threshold=None,
                               max_cat_to_onehot=None, max_delta_step=None,
                               max_depth=None, max_leaves=None,
                               min_child_weight=None, missing=nan,
                               monotone_constraints=None, multi_strategy=None,
                               n_estimators=None, n_jobs=None, num_class=4, ...))])

## 6. Model Training with Cross-Validation

# 1. Prepare for the Experiments
models = {
    "Logistic Regression": lr_pipeline,
    "XGBoost": xgb_pipeline
}
feature_sets = {
    "Aggregate": X_dev_agg,
    "Enriched (Agg + N-grams)": X_dev_enriched
}
cv_strategies = {
    "5-Fold GroupKFold": cv_5fold,
    "6-Fold LOGO": cv_logo_dev
}

y_dev_np = y_dev.to_numpy()
groups_dev_np = groups_dev.to_numpy()
all_classes = np.array(range(NUM_CLASSES))
results = {}

# 2. Run All Cross-Validation Experiments With a Robust Manual Loop

for model_name, model_pipeline in models.items():
    for feature_name, X in feature_sets.items():
        for cv_name, cv_strategy in cv_strategies.items():
            
            experiment_name = f"{model_name} | {feature_name} | {cv_name}"
            print(f"\nRunning: {experiment_name}...")

            fold_scores = []
            n_splits = cv_strategy.get_n_splits(X, y_dev_np, groups_dev_np)
            
            for fold_num, (train_index, test_index) in enumerate(cv_strategy.split(X, y_dev_np, groups=groups_dev_np)):
                
                cloned_pipeline = clone(model_pipeline)
                
                X_train_fold, X_test_fold = X.iloc[train_index] if hasattr(X, 'iloc') else X[train_index], \
                                            X.iloc[test_index] if hasattr(X, 'iloc') else X[test_index]
                y_train_fold, y_test_fold = y_dev_np[train_index], y_dev_np[test_index]

                print(f"--- Fold {fold_num + 1}/{n_splits} ---")
                
                # inserts dummy samples for any missing classes in the training fold
                unique_train_classes = np.unique(y_train_fold)
                
                if len(unique_train_classes) < NUM_CLASSES:
                    print(f"  WARNING: Fold is missing classes. Original unique classes: {unique_train_classes}")
                    missing_classes = np.setdiff1d(all_classes, unique_train_classes)
                    print(f"  Injecting dummy samples for missing classes: {missing_classes}")

                    for missing_class in missing_classes:
                        # Create a dummy feature row (all zeros)
                        if hasattr(X_train_fold, 'toarray'):
                            dummy_row = np.zeros((1, X_train_fold.shape[1]))
                            X_train_fold = sparse_vstack([X_train_fold, dummy_row])
                        else:
                            dummy_row = np.zeros((1, X_train_fold.shape[1]))
                            X_train_fold = np.vstack([X_train_fold, dummy_row])
                        
                        # Add the missing class label
                        y_train_fold = np.append(y_train_fold, missing_class)

                # Fit the model (now guaranteed to see all classes)
                cloned_pipeline.fit(X_train_fold, y_train_fold)
                
                y_pred = cloned_pipeline.predict(X_test_fold)
                score = accuracy_score(y_test_fold, y_pred)
                fold_scores.append(score)
                print(f"  Fold {fold_num + 1} Score: {score:.4f}")

            scores = np.array(fold_scores)
            results[experiment_name] = {
                'mean_accuracy': np.nanmean(scores),
                'std_accuracy': np.nanstd(scores),
                'all_scores': scores
            }

# 3. Display the Results in a Clean Table
results_df = pd.DataFrame.from_dict(results, orient='index')
results_df.sort_values(by='mean_accuracy', ascending=False, inplace=True)

print("\n--- Cross-Validation Results Summary ---")
display(results_df[['mean_accuracy', 'std_accuracy']])

Running: Logistic Regression | Aggregate | 5-Fold GroupKFold...
--- Fold 1/5 ---
  WARNING: Fold is missing classes. Original unique classes: [0 1 3]
  Injecting dummy samples for missing classes: [2]
  Fold 1 Score: 0.2941
--- Fold 2/5 ---
  Fold 2 Score: 0.9512
--- Fold 3/5 ---
  Fold 3 Score: 0.7200
--- Fold 4/5 ---
  Fold 4 Score: 0.9524
--- Fold 5/5 ---
  Fold 5 Score: 0.8387

Running: Logistic Regression | Aggregate | 6-Fold LOGO...
--- Fold 1/6 ---
  Fold 1 Score: 0.7200
--- Fold 2/6 ---
  WARNING: Fold is missing classes. Original unique classes: [0 1 3]
  Injecting dummy samples for missing classes: [2]
  Fold 2 Score: 0.2941
--- Fold 3/6 ---
  Fold 3 Score: 0.8462
--- Fold 4/6 ---
  Fold 4 Score: 0.9512
--- Fold 5/6 ---
  Fold 5 Score: 0.9444
--- Fold 6/6 ---
  Fold 6 Score: 0.9524

Running: Logistic Regression | Enriched (Agg + N-grams) | 5-Fold GroupKFold...
--- Fold 1/5 ---
  WARNING: Fold is missing classes. Original unique classes: [0 1 3]
  Injecting dummy samples for missing classes: [2]
  Fold 1 Score: 0.2941
--- Fold 2/5 ---
  Fold 2 Score: 0.8049
--- Fold 3/5 ---
  Fold 3 Score: 0.9600
--- Fold 4/5 ---
  Fold 4 Score: 0.9524
--- Fold 5/5 ---
  Fold 5 Score: 0.8710

Running: Logistic Regression | Enriched (Agg + N-grams) | 6-Fold LOGO...
--- Fold 1/6 ---
  Fold 1 Score: 0.9600
--- Fold 2/6 ---
  WARNING: Fold is missing classes. Original unique classes: [0 1 3]
  Injecting dummy samples for missing classes: [2]
  Fold 2 Score: 0.2941
--- Fold 3/6 ---
  Fold 3 Score: 0.6923
--- Fold 4/6 ---
  Fold 4 Score: 0.8049
--- Fold 5/6 ---
  Fold 5 Score: 0.9444
--- Fold 6/6 ---
  Fold 6 Score: 0.9524

Running: XGBoost | Aggregate | 5-Fold GroupKFold...
--- Fold 1/5 ---
  WARNING: Fold is missing classes. Original unique classes: [0 1 3]
  Injecting dummy samples for missing classes: [2]
  Fold 1 Score: 0.2745
--- Fold 2/5 ---
  Fold 2 Score: 0.7805
--- Fold 3/5 ---
  Fold 3 Score: 0.9600
--- Fold 4/5 ---
  Fold 4 Score: 0.9048
--- Fold 5/5 ---
  Fold 5 Score: 0.7419

Running: XGBoost | Aggregate | 6-Fold LOGO...
--- Fold 1/6 ---
  Fold 1 Score: 0.9600
--- Fold 2/6 ---
  WARNING: Fold is missing classes. Original unique classes: [0 1 3]
  Injecting dummy samples for missing classes: [2]
  Fold 2 Score: 0.2745
--- Fold 3/6 ---
  Fold 3 Score: 0.6923
--- Fold 4/6 ---
  Fold 4 Score: 0.7805
--- Fold 5/6 ---
  Fold 5 Score: 0.9444
--- Fold 6/6 ---
  Fold 6 Score: 0.9048

Running: XGBoost | Enriched (Agg + N-grams) | 5-Fold GroupKFold...
--- Fold 1/5 ---
  WARNING: Fold is missing classes. Original unique classes: [0 1 3]
  Injecting dummy samples for missing classes: [2]
  Fold 1 Score: 0.2745
--- Fold 2/5 ---
  Fold 2 Score: 0.8293
--- Fold 3/5 ---
  Fold 3 Score: 0.9600
--- Fold 4/5 ---
  Fold 4 Score: 0.9048
--- Fold 5/5 ---
  Fold 5 Score: 0.7742

Running: XGBoost | Enriched (Agg + N-grams) | 6-Fold LOGO...
--- Fold 1/6 ---
  Fold 1 Score: 0.9600
--- Fold 2/6 ---
  WARNING: Fold is missing classes. Original unique classes: [0 1 3]
  Injecting dummy samples for missing classes: [2]
  Fold 2 Score: 0.2745
--- Fold 3/6 ---
  Fold 3 Score: 0.6154
--- Fold 4/6 ---
  Fold 4 Score: 0.8293
--- Fold 5/6 ---
  Fold 5 Score: 0.9444
--- Fold 6/6 ---
  Fold 6 Score: 0.9048

--- Cross-Validation Results Summary ---

from sklearn.model_selection import GridSearchCV

# The best model from Section 6 was Logistic Regression with the Aggregate feature set and 6-Fold LOGO CV.
tuning_pipeline = lr_pipeline

# Define the grid of hyperparameters to search for Logistic Regression.
param_grid = {
    'logreg__C': [0.01, 0.1, 1, 10, 100]
}

# Set up GridSearchCV using the group-aware CV strategy.
grid_search = GridSearchCV(
    estimator=tuning_pipeline,
    param_grid=param_grid,
    cv=cv_logo_dev,
    scoring='accuracy',
    n_jobs=-1,
    verbose=1
)

# Run the search on the development data with the aggregate feature set.
grid_search.fit(X_dev_agg, y_dev, groups=groups_dev)

print("\n--- GridSearchCV Results ---")
print(f"Best cross-validated accuracy: {grid_search.best_score_:.4f}")
print("Best hyperparameters found:")
print(grid_search.best_params_)

# 6. Store the best model for the final evaluation in Section 7
best_tuned_model = grid_search.best_estimator_

Fitting 6 folds for each of 5 candidates, totalling 30 fits

--- GridSearchCV Results ---
Best cross-validated accuracy: 0.7949
Best hyperparameters found:
{'logreg__C': 100}

FUNCTION GridSearchCV(estimator, param_grid, X_data, y_data, cv_splitter, scoring_function):

  // Initialize variables to keep track of the best results
  best_score = -infinity
  best_params = null
  all_results = []

  // Generate all unique combinations of hyperparameters from the param_grid
  parameter_combinations = generate_combinations(param_grid)

  // --- Main Loop: Iterate over each hyperparameter combination ---
  FOR EACH current_params IN parameter_combinations:
    
    // Create a list to store the scores for each fold
    fold_scores = []

    // Get the data splits (e.g., 5 folds of train/validation indices)
    cv_splits = cv_splitter.get_splits(X_data, y_data)

    // --- Cross-Validation Loop: Iterate over each fold ---
    FOR EACH (train_indices, validation_indices) IN cv_splits:

      // 1. Split the data for this fold
      X_train, y_train = get_data_from_indices(X_data, y_data, train_indices)
      X_validation, y_validation = get_data_from_indices(X_data, y_data, validation_indices)

      // 2. Create a new instance of the model to ensure it's untrained
      model = create_new_instance(estimator)

      // 3. Set the current hyperparameters on the new model
      model.set_parameters(current_params)

      // 4. Train the model on the training data for this fold
      model.fit(X_train, y_train)

      // 5. Evaluate the model on the validation data
      predictions = model.predict(X_validation)
      score = scoring_function(y_validation, predictions)

      // 6. Add the score for this fold to the list
      APPEND score TO fold_scores

    // --- End of Cross-Validation Loop ---

    // Calculate the average score across all folds for the current_params
    average_score = calculate_average(fold_scores)
    
    // Store the results for inspection later
    APPEND {params: current_params, score: average_score} TO all_results

    // Check if this combination of parameters is the best one so far
    IF average_score > best_score:
      best_score = average_score
      best_params = current_params

  // --- End of Main Loop ---

  // After checking all combinations, refit the model on the ENTIRE dataset
  // using the best parameters found
  best_estimator = create_new_instance(estimator)
  best_estimator.set_parameters(best_params)
  best_estimator.fit(X_data, y_data)

  // Return the best findings
  RETURN {
    'best_params_': best_params,
    'best_score_': best_score,
    'best_estimator_': best_estimator,
    'all_results_': all_results
  }

END FUNCTION

# 1. Use the best tuned model to predict on the test set
final_predictions_encoded = best_tuned_model.predict(X_test_agg)

# 2. Compute and Visualize the Classification Report
report_dict = classification_report(
    y_true=y_test, 
    y_pred=final_predictions_encoded, 
    target_names=le.classes_,
    output_dict=True
)
report_df = pd.DataFrame(report_dict).transpose()
report_df_for_plot = report_df.drop(columns='support')

plt.figure(figsize=(10, 6))
sns.heatmap(
    report_df_for_plot.iloc[:-3, :],
    annot=True, 
    cmap='RdYlGn',
    fmt='.2f',
    vmin=0.5,
    vmax=1.0
)
plt.title('Final Performance Metrics on Test Set', fontsize=16)
plt.show()

print(classification_report(y_true=y_test, y_pred=final_predictions_encoded, target_names=le.classes_))

                      precision    recall  f1-score   support

Baroness Von Bon Bon       0.88      1.00      0.93        14
    Cagney Carnation       0.78      1.00      0.88        18
 Glumstone the Giant       1.00      0.67      0.80        24
     Grim Matchstick       0.94      1.00      0.97        15

            accuracy                           0.89        71
           macro avg       0.90      0.92      0.89        71
        weighted avg       0.91      0.89      0.88        71

# 3. Visualize the Confusion Matrix for the Test Set Predictions 
fig, ax = plt.subplots(figsize=(8, 8))
ConfusionMatrixDisplay.from_predictions(
    y_true=y_test, 
    y_pred=final_predictions_encoded,
    display_labels=le.classes_,
    cmap='Blues',
    ax=ax
)
ax.set_title('Confusion Matrix on Test Set')
plt.xticks(rotation=45, ha='right')
plt.show()

# 1. Load the New Holdout Data from the New Directory
new_data_path = '../data/raw_new/*.jsonl'
new_files = glob.glob(new_data_path)
new_events_data = []

print(f"Loading {len(new_files)} new fights from the holdout set...")


# Load and process each new file
for file_path in new_files:
    with open(file_path, 'r') as f:
        lines = f.readlines()
        if not lines: continue
        try:
            meta_data = json.loads(lines[0])['meta']
            fight_id = json.loads(lines[0])['fight_id']
            boss_name = meta_data.get('boss')
            for line in lines[1:-1]:
                event_data = json.loads(line)
                if 'event' in event_data:
                    new_events_data.append({
                        'fight_id': fight_id, 'boss': boss_name,
                        'event': event_data['event'], 'key': event_data['key'],
                        't_ms': event_data['t_ms']
                    })
        except (json.JSONDecodeError, KeyError) as e:
            print(f"Skipping malformed file in new data: {file_path}")

raw_df_new = pd.DataFrame(new_events_data)

# 2. Apply the same Pre-processing and Feature Engineering Pipeline (Section 2 & 3)

# Adjust timestamps
first_action_times_new = raw_df_new.groupby('fight_id')['t_ms'].transform('min')
clean_df_new = raw_df_new.copy()
clean_df_new['t_ms'] = clean_df_new['t_ms'] - first_action_times_new

# Standardize action names
processed_df_new = clean_df_new.copy()
processed_df_new['action'] = processed_df_new['key'].map(key_mapping)
processed_df_new.dropna(subset=['action'], inplace=True)

# Engineer Aggregate features using the same functions and logic
press_durations_df_new = calculate_all_press_durations(processed_df_new)
keydowns_new = processed_df_new[processed_df_new['event'] == 'keydown']
fight_summaries_new = keydowns_new.groupby('fight_id').agg(
    duration_ms=('t_ms', 'max'), event_count=('t_ms', 'count'), boss=('boss', 'first')
)
fight_summaries_new['apm'] = (fight_summaries_new['event_count'] / (fight_summaries_new['duration_ms'] / 1000.0)) * 60
action_percentages_new = keydowns_new.groupby(['fight_id', 'action']).size().unstack(fill_value=0).div(fight_summaries_new['event_count'], axis=0).add_prefix('pct_')
overall_duration_stats_new = press_durations_df_new.groupby('fight_id')['duration_ms'].agg(['mean', 'std', 'median', 'min', 'max']).add_prefix('overall_duration_')
action_specific_stats_new = press_durations_df_new.groupby(['fight_id', 'action'])['duration_ms'].agg(['mean', 'std']).unstack().fillna(0)
action_specific_stats_new.columns = ['_'.join(col).strip() for col in action_specific_stats_new.columns.values]

agg_features_new = fight_summaries_new.join(action_percentages_new).join(overall_duration_stats_new).join(action_specific_stats_new).fillna(0)

# Ensure the holdout feature set has the exact same columns as the training set
X_holdout_agg = agg_features_new.reindex(columns=X_agg.columns, fill_value=0)
y_holdout_str = agg_features_new['boss']

# Use the previously fitted LabelEncoder to transform the new labels
y_holdout_encoded = le.transform(y_holdout_str)

# --- 3. Retrain the Best Model on the Full Original Dataset ---
print("\nRetraining the best model on the full original 240-fight dataset...")
# Clone the model to ensure it's a fresh instance
final_model_for_holdout = clone(best_tuned_model)
# Train on the full original dataset (X_agg and y)
final_model_for_holdout.fit(X_agg, y)
print("Retraining complete.")

# 4. Make Predictions and Evaluate on the New Holdout Data
holdout_predictions = final_model_for_holdout.predict(X_holdout_agg)

# Calculate and print the final performance
holdout_accuracy = accuracy_score(y_holdout_encoded, holdout_predictions)

print("\n--- Final Performance on New Holdout Set (from 2025/10/19) ---")
print(f"Accuracy on truly unseen data: {holdout_accuracy:.4f}")
print("\nClassification Report:")
print(classification_report(y_holdout_encoded, holdout_predictions, target_names=le.classes_))

# 5. Visualize the Performance on the Holdout Set

# A. Performance Metrics Heatmap
print("\n--- Visual Performance Report on Holdout Set ---")
report_dict_holdout = classification_report(
    y_true=y_holdout_encoded, 
    y_pred=holdout_predictions, 
    target_names=le.classes_,
    output_dict=True
)
report_df_holdout = pd.DataFrame(report_dict_holdout).transpose()
report_df_for_plot_holdout = report_df_holdout.drop(columns='support')

plt.figure(figsize=(10, 6))
sns.heatmap(
    report_df_for_plot_holdout.iloc[:-3, :],
    annot=True, 
    cmap='RdYlGn',
    fmt='.2f',
    vmin=0.5,
    vmax=1.0
)
plt.title('Performance Metrics on New Holdout Set (2025/10/19)', fontsize=16)
plt.show()


# B. Confusion Matrix
print("\n--- Confusion Matrix for Holdout Set ---")
fig, ax = plt.subplots(figsize=(8, 8))
ConfusionMatrixDisplay.from_predictions(
    y_true=y_holdout_encoded, 
    y_pred=holdout_predictions,
    display_labels=le.classes_,
    cmap='Blues',
    ax=ax
)
ax.set_title('Confusion Matrix on New Holdout Set (2025/10/19)')
plt.xticks(rotation=45, ha='right')
plt.show()

Loading 28 new fights from the holdout set...

Retraining the best model on the full original 240-fight dataset...
Retraining complete.

--- Final Performance on New Holdout Set (from 2025/10/19) ---
Accuracy on truly unseen data: 0.7857

Classification Report:
                      precision    recall  f1-score   support

Baroness Von Bon Bon       1.00      0.71      0.83         7
    Cagney Carnation       0.70      1.00      0.82         7
 Glumstone the Giant       0.80      0.57      0.67         7
     Grim Matchstick       0.75      0.86      0.80         7

            accuracy                           0.79        28
           macro avg       0.81      0.79      0.78        28
        weighted avg       0.81      0.79      0.78        28


--- Visual Performance Report on Holdout Set ---

--- Confusion Matrix for Holdout Set ---

# 1. Data-Driven Feature Selection Using Model Coefficients
# Extract feature importance from the final logistic regression model
final_logreg_model = best_tuned_model.named_steps['logreg']
feature_names = X_agg.columns.tolist()

# Create a DataFrame of coefficients for easier analysis
coef_df = pd.DataFrame(final_logreg_model.coef_, columns=feature_names, index=le.classes_)
# Calculate the standard deviation of coefficients across classes as a measure of importance
feature_importance = coef_df.std(axis=0).sort_values(ascending=False)
features_to_compare = feature_importance.head(5).index.tolist()

# 2. Isolate All Relevant Groups from the Test Set
analysis_df_test = agg_features.loc[y_test.index].copy()
analysis_df_test['true_label'] = le.inverse_transform(y_test)
analysis_df_test['predicted_label'] = le.inverse_transform(final_predictions_encoded)
analysis_df_test['status'] = np.where(analysis_df_test['true_label'] == analysis_df_test['predicted_label'], 'Correct', 'Incorrect')

# Isolate the four groups of interest
glumstone_correct = analysis_df_test[(analysis_df_test['true_label'] == 'Glumstone the Giant') & (analysis_df_test['status'] == 'Correct')].copy()
cagney_correct = analysis_df_test[(analysis_df_test['true_label'] == 'Cagney Carnation') & (analysis_df_test['status'] == 'Correct')].copy()
bonbon_correct = analysis_df_test[(analysis_df_test['true_label'] == 'Baroness Von Bon Bon') & (analysis_df_test['status'] == 'Correct')].copy()
glumstone_misclassified = analysis_df_test[(analysis_df_test['true_label'] == 'Glumstone the Giant') & (analysis_df_test['status'] == 'Incorrect')].copy()

glumstone_correct['group'] = 'Correct Glumstone'
cagney_correct['group'] = 'Correct Cagney'
bonbon_correct['group'] = 'Correct Bon Bon'
glumstone_misclassified['group'] = 'Misclassified Glumstone'

comparison_df = pd.concat([glumstone_correct, cagney_correct, bonbon_correct, glumstone_misclassified])

# 3. Create the Side-by-Side Diagnostic Plots
fig, axes = plt.subplots(1, 5, figsize=(30, 7), sharey=False)
fig.suptitle('Diagnosing Misclassifications Using Top 5 Model Features', fontsize=24)
palette = {
    'Correct Glumstone': '#4daf4a', 
    'Misclassified Glumstone': '#e41a1c', 
    'Correct Cagney': '#377eb8',
    'Correct Bon Bon': '#ff7f00'
}

# Plot each feature
for i, feature in enumerate(features_to_compare):
    ax = axes[i]
    sns.stripplot(data=comparison_df, x='group', y=feature, ax=ax, palette=palette, jitter=0.25, alpha=0.7, 
                  order=['Correct Glumstone', 'Misclassified Glumstone', 'Correct Cagney', 'Correct Bon Bon'])
    sns.boxplot(data=comparison_df, x='group', y=feature, ax=ax, boxprops=dict(alpha=.3, facecolor='none', edgecolor='black'), 
                showfliers=False, order=['Correct Glumstone', 'Misclassified Glumstone', 'Correct Cagney', 'Correct Bon Bon'])
    ax.set_title(f'Distribution of "{feature}"', fontsize=16)
    ax.set_xlabel(None)
    ax.set_ylabel('Feature Value', fontsize=12)
    ax.tick_params(axis='x', rotation=45, labelsize=12)

fig.supxlabel('Fight Group', fontsize=16, y=0.02)
plt.tight_layout(rect=[0, 0.05, 1, 0.95])
plt.show()

# 1. Pre-processing: Standardize Action Names
key_mapping = {
    'Key.space': 'jump', 'f': 'shoot', 'd': 'dash', 'x': 'lock',
    'a': 'special attack', 'Key.up': 'up', 'Key.down': 'down', 'Key.left': 'left', 'Key.right': 'right'
}
processed_df = final_df.copy()
processed_df['action'] = processed_df['key'].map(key_mapping)
# Drop any events that weren't in the mapping
processed_df.dropna(subset=['action'], inplace=True)


# EDA: Visualizing the Raw Keystroke Timeline
# Gather fight outcomes and durations from the summary lines
fight_outcomes = {}
for file_path in glob.glob('../data/raw/*.jsonl'):
    fight_id = file_path.split('/')[-1].replace('.jsonl', '')
    with open(file_path, 'r') as f:
        try:
            last_line = f.readlines()[-1]
            summary = json.loads(last_line)['summary']
            fight_outcomes[fight_id] = {
                "outcome": summary.get('outcome'),
                "duration_ms": summary.get('duration_ms')
            }
        except (IndexError, json.JSONDecodeError, KeyError):
            continue

# Convert fight outcomes and durations to DataFrame for easy merging
outcomes_df = pd.DataFrame.from_dict(fight_outcomes, orient='index').reset_index().rename(columns={'index': 'fight_id'})

fight_ids_for_plot = []
boss_names = processed_df['boss'].unique()

for boss in boss_names:
    # Get all fight IDs for the current boss from the sampled dataset
    boss_fight_ids = processed_df[processed_df['boss'] == boss]['fight_id'].unique()
    
    # Merge with outcomes and durations
    boss_fights_df = outcomes_df[outcomes_df['fight_id'].isin(boss_fight_ids)]
    
    # Try to find the longest 'win'
    win_fights = boss_fights_df[boss_fights_df['outcome'] == 'win']
    
    if not win_fights.empty:
        # If there are wins, get the ID of the longest one
        longest_win_id = win_fights.sort_values('duration_ms', ascending=False).iloc[0]['fight_id']
        fight_ids_for_plot.append(longest_win_id)
    else:
        # If no wins, get the ID of the longest fight overall for that boss
        longest_fight_id = boss_fights_df.sort_values('duration_ms', ascending=False).iloc[0]['fight_id']
        fight_ids_for_plot.append(longest_fight_id)

# Prepare Color Mapping
all_possible_actions = sorted(processed_df['action'].unique(), reverse=True)
palette = sns.color_palette("husl", len(all_possible_actions))
global_color_map = dict(zip(all_possible_actions, palette))
global_action_map = {action: i for i, action in enumerate(all_possible_actions)}

def plot_fight_timeline(df, fight_id, ax, action_order, action_map, color_map):
    """Generates a Gantt-style chart with a consistent y-axis."""
    fight_data = df[df['fight_id'] == fight_id].copy()
    boss_name = fight_data['boss'].iloc[0] # Get boss name early
    ax.set_title(f"Keystroke Timeline for {boss_name}")
    
    # Calculate durations for this fight
    downs = fight_data[fight_data['event'] == 'keydown'].copy()
    ups = fight_data[fight_data['event'] == 'keyup'].copy()
    downs['press_num'] = downs.groupby(['fight_id', 'action']).cumcount()
    ups['press_num'] = ups.groupby(['fight_id', 'action']).cumcount()
    merged = pd.merge(downs, ups, on=['fight_id', 'boss', 'action', 'press_num'], suffixes=('_start', '_end'))
    durations = merged[merged['t_ms_end'] > merged['t_ms_start']].copy()
    
    # Only try to plot if there are valid durations to show
    if not durations.empty:
        durations['duration'] = durations['t_ms_end'] - durations['t_ms_start']
        bar_colors = durations['action'].map(color_map)
        ax.barh(y=durations['action'].map(action_map), 
                left=durations['t_ms_start'], 
                width=durations['duration'], 
                height=0.5,
                color=bar_colors)
            
    # Set consistent y-axis
    ax.set_yticks(range(len(action_order)))
    ax.set_yticklabels(action_order)
    ax.set_xlabel("Time (ms)")
    ax.grid(axis='x', linestyle='--', alpha=0.6)

# Create subplots for each selected fight
fig, axes = plt.subplots(len(fight_ids_for_plot), 1, figsize=(15, 12), sharex=True)
fig.suptitle('EDA: Keystroke Timelines for Longest Successful Fights', fontsize=16)

for i, fight_id in enumerate(fight_ids_for_plot):
    plot_fight_timeline(processed_df, fight_id, axes[i], all_possible_actions, global_action_map, global_color_map)

plt.tight_layout(rect=[0, 0.03, 1, 0.95])
plt.show()

# EDA: Generalized Heatmap of Press Durations

# calculate the press durations for all fights in the dataset
all_downs = processed_df[processed_df['event'] == 'keydown'].copy()
all_ups = processed_df[processed_df['event'] == 'keyup'].copy()

# Group by fight_id and action to correctly match each press to its release
all_downs['press_num'] = all_downs.groupby(['fight_id', 'action']).cumcount()
all_ups['press_num'] = all_ups.groupby(['fight_id', 'action']).cumcount()

all_merged = pd.merge(all_downs, all_ups, on=['fight_id', 'boss', 'action', 'press_num'], suffixes=('_down', '_up'))
all_merged['duration_ms'] = all_merged['t_ms_up'] - all_merged['t_ms_down']
all_durations = all_merged[all_merged['duration_ms'] >= 0]

# Create a pivot table to get the average duration for each (Boss, Action) pair
pivot_df = all_durations.groupby(['boss', 'action'])['duration_ms'].mean().unstack()

# Plot the Heatmap
plt.figure(figsize=(12, 8))
sns.heatmap(
    pivot_df, 
    annot=True,
    fmt=".0f",
    cmap="viridis",
    norm=LogNorm(),
    cbar_kws={'label': 'Avg Duration (ms) - Log Scale'}
)

plt.title('Generalizable EDA: Average Key Press Duration by Boss and Action', fontsize=16)
plt.xlabel('Action', fontsize=12)
plt.ylabel('Boss', fontsize=12)
plt.xticks(rotation=45, ha="right")
plt.tight_layout()
plt.show()

# EDA: Visualizing Fight Duration Distribution by Outcome
fight_summaries_for_plot = final_df.groupby('fight_id').agg(
    duration_ms=('t_ms', 'max'),
    boss=('boss', 'first')
).reset_index()

fight_summaries_for_plot['duration_s'] = fight_summaries_for_plot['duration_ms'] / 1000
# Merge with outcomes to get the outcome for each fight
plot_data = pd.merge(fight_summaries_for_plot, outcomes_df, on='fight_id', suffixes=('', '_from_summary'))
plt.figure(figsize=(14, 8))
outcome_palette = {'win': 'green', 'lose': 'red'}

# draw the boxplots to show the summary statistics
sns.boxplot(
    data=plot_data, 
    y='boss', 
    x='duration_s', 
    orient='h',
    showfliers=False,  # Hide outlier points from the boxplot itself
    boxprops=dict(alpha=.3) # Make boxes slightly transparent
)

# overlay the stripplot to show every individual fight
sns.stripplot(
    data=plot_data, 
    y='boss', 
    x='duration_s', 
    hue='outcome',
    orient='h',
    jitter=0.2,
    alpha=0.7,
    palette=outcome_palette
)

plt.title('Distribution of Fight Durations by Boss and Outcome', fontsize=16)
plt.xlabel('Fight Duration (seconds)', fontsize=12)
plt.ylabel('Boss', fontsize=12)
plt.grid(axis='x', linestyle='--', alpha=0.7)
plt.legend(title='Outcome')
plt.tight_layout()
plt.show()

	apm	pct_dash	pct_down	pct_jump	pct_left	pct_lock	pct_right	pct_shoot	pct_special attack	pct_up	...	mean_up	std_dash	std_down	std_jump	std_left	std_lock	std_right	std_shoot	std_special attack	std_up
fight_id
2025-09-23T13-51-00Z_60041	114.321347	0.018182	0.000000	0.400000	0.172727	0.018182	0.309091	0.027273	0.036364	0.018182	...	1711.0	0.000000	0.000000	63.923063	189.790312	1195.01046	455.944910	28987.901103	19.131126	547.300649
2025-09-23T13-53-53Z_33477	115.224478	0.042017	0.000000	0.403361	0.184874	0.000000	0.285714	0.008403	0.075630	0.000000	...	0.0	6.685806	0.000000	51.093156	271.200633	0.00000	246.356487	0.000000	11.938500	0.000000
2025-09-23T13-55-01Z_1585	106.557652	0.047170	0.000000	0.377358	0.150943	0.009434	0.301887	0.018868	0.075472	0.018868	...	333.0	20.329781	0.000000	36.973232	276.202945	0.00000	215.707517	41828.194534	14.267220	25.455844
2025-09-23T13-57-05Z_25486	102.404096	0.031250	0.000000	0.437500	0.132812	0.000000	0.328125	0.015625	0.054688	0.000000	...	0.0	4.830459	0.000000	59.789476	316.814719	0.00000	219.255757	23143.604948	25.967012	0.000000
2025-09-23T14-02-29Z_49949	124.101400	0.060976	0.036585	0.353659	0.109756	0.000000	0.390244	0.012195	0.036585	0.000000	...	0.0	25.351529	557.311702	53.159788	121.180421	0.00000	301.333428	0.000000	17.521415	0.000000

	mean_accuracy	std_accuracy
Logistic Regression \| Aggregate \| 6-Fold LOGO	0.784719	0.234520
Logistic Regression \| Enriched (Agg + N-grams) \| 5-Fold GroupKFold	0.776469	0.247802
Logistic Regression \| Enriched (Agg + N-grams) \| 6-Fold LOGO	0.774688	0.235718
XGBoost \| Aggregate \| 6-Fold LOGO	0.759419	0.236529
XGBoost \| Enriched (Agg + N-grams) \| 6-Fold LOGO	0.754728	0.243660
Logistic Regression \| Aggregate \| 5-Fold GroupKFold	0.751286	0.244146
XGBoost \| Enriched (Agg + N-grams) \| 5-Fold GroupKFold	0.748547	0.245357
XGBoost \| Aggregate \| 5-Fold GroupKFold	0.732339	0.242313

Feature Strategy	Pros	Cons	Status	How Cons are Addressed
Fixed Time Slices	- Attempts to capture temporal evolution of gameplay.	- Creates sparse/misaligned vectors from variable-length data. - High risk of overfitting due to feature explosion.	Rejected	N/A
Proportional Time Slicing	- Solves the variable-length issue by dividing fights into percentage-based chunks (ㄷㅌ. 10 chunks of 10% each).	- Boss phases are triggered by damage, not time. A 10% time slice might be phase 1 in a long fight but phase 2 in a short one, making the features non-comparable.	Rejected	N/A
Recurrent Neural Networks (RNN/LSTM)	- The most powerful method for modeling raw sequential data directly.	- High implementation complexity (padding, vectorization). - Prone to overfitting on smaller datasets (240 samples).	Rejected (For this assignment)	N/A (A great candidate for future work with more data.)
Aggregate Statistical Features	- Directly captures variability (`std`, `median`). - Creates a dense, fixed-size vector for every fight. - Robust to outliers and varying fight lengths.	- Loses all information about the sequence of actions (ex. is a `dash` usually preceded by a `jump`?).	Accepted (Foundational)	This con is directly complemented by using N-gram Features.
N-gram Sequence Features	- Captures the order and rhythm of common action combos. - Adds crucial sequential context to the model.	- Loses all precise timing information (e.g., the duration of the presses or the time between them).	Accepted (Enrichment)	This con is directly complemented by the Aggregate Statistical Features.

	fight_id	boss	event	key	t_ms
0	2025-09-24T10-17-07Z_27194	Glumstone the Giant	keydown	f	0
1	2025-09-24T10-17-07Z_27194	Glumstone the Giant	keydown	Key.down	45
2	2025-09-24T10-17-07Z_27194	Glumstone the Giant	keyup	Key.down	332
3	2025-09-24T10-17-07Z_27194	Glumstone the Giant	keyup	f	403
4	2025-09-24T10-17-07Z_27194	Glumstone the Giant	keydown	Key.down	543
5	2025-09-24T10-17-07Z_27194	Glumstone the Giant	keydown	f	544
6	2025-09-24T10-17-07Z_27194	Glumstone the Giant	keyup	Key.down	1334
7	2025-09-24T10-17-07Z_27194	Glumstone the Giant	keyup	f	1410
8	2025-09-24T10-17-07Z_27194	Glumstone the Giant	keydown	Key.down	1454
9	2025-09-24T10-17-07Z_27194	Glumstone the Giant	keydown	f	2130

boss_encoded	0	1	2	3
fight_id
2025-09-23	0	4	0	21
2025-09-24	5	0	24	15
2025-09-26	15	0	36	0
2025-09-27	0	13	0	0
2025-09-29	28	13	0	0
2025-10-04	9	18	0	0
2025-10-08	0	12	0	6
2025-10-14	3	0	0	18

Predicting Cuphead Bosses from Keystroke Data¶

1. Introduction / The Dataset¶

2. Loading the Raw Data¶

Data Cleaning¶

3. Pre-processing and Feature Engineering¶

Aggregate Statistical Features¶

N-gram¶

N-gram Visualization¶

Final Feature List (with an example fight)¶

Analysis Plan¶

Data Splitting Strategy¶

5. Model Selection and Mathematical Underpinnings¶

Mathematical Explanation¶

Multinomial Logistic Regression¶

XGBoost (eXtreme Gradient Boosting)¶

6. Model Training with Cross-Validation¶

Justification for the Dummy Sample Injection¶

6.1 Hyperparameter Tuning the Best Model¶

GridSearchCV Pseudo code¶

7. Generating Out-of-Sample Predictions and Performance Metrics¶

7.1 Performance on the Untouched Test Set¶

Analysis of Misclassified Fights¶

7.2 Final Validation on a Truly Unseen Dataset (Extension)¶

8. Discussion and Conclusions¶

9. Executive Summary¶

Pipeline Overview¶

Key Results and Insights¶

Shortcomings and Future Improvements¶

References¶

Appendices¶

Appendix A: How the Data Was Obtained and Sampled¶

A-1. Data Logger App¶

A-2. Demo Video¶

Data Collection Recording (Old UI - 1h 18 min)¶

Data Collection Recording (Latest UI - 8 min)¶

A-3. UI Design Evolution¶

Appendix B: EDA Process & Analysis¶

Analysis of Single-Fight Timelines¶

Aggregated Heatmap¶

Analysis of Generalized Patterns¶

B-2 Feature Strategy¶

Appendix B: N-gram Mathematical Explanation & Pseudocode¶